{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "S+P Week 2 Lesson 2.ipynb",
      "version": "0.3.2",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "D1J15Vh_1Jih",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "outputId": "21e8e480-aa62-4a88-e6a8-604df9d4bfd2"
      },
      "source": [
        "!pip install tf-nightly-2.0-preview\n"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting tf-nightly-2.0-preview\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/b8/48/ba86399b1e11ec23bc3243de942c5bccd7f1471f392d5c5aea39033e7dc4/tf_nightly_2.0_preview-2.0.0.dev20190917-cp36-cp36m-manylinux2010_x86_64.whl (91.3MB)\n",
            "\u001b[K     |████████████████████████████████| 91.3MB 1.3MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy<2.0,>=1.16.0 in /usr/local/lib/python3.6/dist-packages (from tf-nightly-2.0-preview) (1.16.5)\n",
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            "Collecting tensorflow-estimator-2.0-preview (from tf-nightly-2.0-preview)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/20/97/d6cb14342da0206236e6a6da5e57cfb634c781d9c82dec1f7e319a72bb47/tensorflow_estimator_2.0_preview-1.14.0.dev2019091700-py2.py3-none-any.whl (450kB)\n",
            "\u001b[K     |████████████████████████████████| 450kB 38.9MB/s \n",
            "\u001b[?25hRequirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tf-nightly-2.0-preview) (1.11.2)\n",
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            "Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tf-nightly-2.0-preview) (0.8.0)\n",
            "Collecting tb-nightly<2.1.0a0,>=2.0.0a0 (from tf-nightly-2.0-preview)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/2c/1c/194dd57b4164caf8cf149843c6c240477bd7c8e91c9f7a842ea3f7f114b2/tb_nightly-2.0.0a20190917-py3-none-any.whl (3.8MB)\n",
            "\u001b[K     |████████████████████████████████| 3.8MB 48.2MB/s \n",
            "\u001b[?25hRequirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tf-nightly-2.0-preview) (3.7.1)\n",
            "Requirement already satisfied: keras-applications>=1.0.8 in /usr/local/lib/python3.6/dist-packages (from tf-nightly-2.0-preview) (1.0.8)\n",
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            "Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tf-nightly-2.0-preview) (0.8.0)\n",
            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tf-nightly-2.0-preview) (1.12.0)\n",
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            "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tf-nightly-2.0-preview) (1.1.0)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<2.1.0a0,>=2.0.0a0->tf-nightly-2.0-preview) (0.15.6)\n",
            "Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<2.1.0a0,>=2.0.0a0->tf-nightly-2.0-preview) (41.2.0)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<2.1.0a0,>=2.0.0a0->tf-nightly-2.0-preview) (3.1.1)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.8->tf-nightly-2.0-preview) (2.8.0)\n",
            "Installing collected packages: tensorflow-estimator-2.0-preview, tb-nightly, tf-nightly-2.0-preview\n",
            "Successfully installed tb-nightly-2.0.0a20190917 tensorflow-estimator-2.0-preview-1.14.0.dev2019091700 tf-nightly-2.0-preview-2.0.0.dev20190917\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BOjujz601HcS",
        "colab_type": "code",
        "outputId": "8f448ff5-baf4-40e5-b622-9089ad562df0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "print(tf.__version__)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.0.0-dev20190917\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Zswl7jRtGzkk",
        "colab": {}
      },
      "source": [
        "def plot_series(time, series, format=\"-\", start=0, end=None):\n",
        "    plt.plot(time[start:end], series[start:end], format)\n",
        "    plt.xlabel(\"Time\")\n",
        "    plt.ylabel(\"Value\")\n",
        "    plt.grid(True)\n",
        "\n",
        "def trend(time, slope=0):\n",
        "    return slope * time\n",
        "\n",
        "def seasonal_pattern(season_time):\n",
        "    \"\"\"Just an arbitrary pattern, you can change it if you wish\"\"\"\n",
        "    return np.where(season_time < 0.4,\n",
        "                    np.cos(season_time * 2 * np.pi),\n",
        "                    1 / np.exp(3 * season_time))\n",
        "\n",
        "def seasonality(time, period, amplitude=1, phase=0):\n",
        "    \"\"\"Repeats the same pattern at each period\"\"\"\n",
        "    season_time = ((time + phase) % period) / period\n",
        "    return amplitude * seasonal_pattern(season_time)\n",
        "\n",
        "def noise(time, noise_level=1, seed=None):\n",
        "    rnd = np.random.RandomState(seed)\n",
        "    return rnd.randn(len(time)) * noise_level\n",
        "\n",
        "time = np.arange(4 * 365 + 1, dtype=\"float32\")\n",
        "baseline = 10\n",
        "series = trend(time, 0.1)  \n",
        "baseline = 10\n",
        "amplitude = 40\n",
        "slope = 0.05\n",
        "noise_level = 5\n",
        "\n",
        "# Create the series\n",
        "series = baseline + trend(time, slope) + seasonality(time, period=365, amplitude=amplitude)\n",
        "# Update with noise\n",
        "series += noise(time, noise_level, seed=42)\n",
        "\n",
        "split_time = 1000\n",
        "time_train = time[:split_time]\n",
        "x_train = series[:split_time]\n",
        "time_valid = time[split_time:]\n",
        "x_valid = series[split_time:]\n",
        "\n",
        "window_size = 20\n",
        "batch_size = 32\n",
        "shuffle_buffer_size = 1000"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4sTTIOCbyShY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def windowed_dataset(series, window_size, batch_size, shuffle_buffer):\n",
        "  dataset = tf.data.Dataset.from_tensor_slices(series)\n",
        "  dataset = dataset.window(window_size + 1, shift=1, drop_remainder=True)\n",
        "  dataset = dataset.flat_map(lambda window: window.batch(window_size + 1))\n",
        "  dataset = dataset.shuffle(shuffle_buffer).map(lambda window: (window[:-1], window[-1]))\n",
        "  dataset = dataset.batch(batch_size).prefetch(1)\n",
        "  return dataset"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ou-WmE2AXu6B",
        "colab_type": "code",
        "outputId": "3a069cd8-1bcc-4f08-f683-2923fb8c76e0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        }
      },
      "source": [
        "dataset = windowed_dataset(x_train, window_size, batch_size, shuffle_buffer_size)\n",
        "print(dataset)\n",
        "l0 = tf.keras.layers.Dense(1, input_shape=[window_size])\n",
        "model = tf.keras.models.Sequential([l0])\n",
        "\n",
        "\n",
        "model.compile(loss=\"mse\", optimizer=tf.keras.optimizers.SGD(lr=1e-6, momentum=0.9))\n",
        "model.fit(dataset,epochs=100,verbose=0)\n",
        "\n",
        "print(\"Layer weights {}\".format(l0.get_weights()))\n",
        "\n"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<PrefetchDataset shapes: ((None, None), (None,)), types: (tf.float32, tf.float32)>\n",
            "Layer weights [array([[ 0.02966811],\n",
            "       [-0.11396817],\n",
            "       [ 0.03013613],\n",
            "       [ 0.05925332],\n",
            "       [ 0.04487267],\n",
            "       [-0.03282484],\n",
            "       [-0.01954184],\n",
            "       [ 0.07706827],\n",
            "       [-0.04144632],\n",
            "       [-0.04573438],\n",
            "       [-0.00734905],\n",
            "       [ 0.06541742],\n",
            "       [ 0.003009  ],\n",
            "       [-0.04243594],\n",
            "       [ 0.03498671],\n",
            "       [ 0.03347524],\n",
            "       [ 0.07557578],\n",
            "       [ 0.24008925],\n",
            "       [ 0.27052268],\n",
            "       [ 0.37939587]], dtype=float32), array([0.01613745], dtype=float32)]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-gtVJuLVxR-M",
        "colab_type": "code",
        "outputId": "2277afb6-4b43-4443-a4ca-e5df508decbe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        }
      },
      "source": [
        "forecast = []\n",
        "\n",
        "for time in range(len(series) - window_size):\n",
        "  forecast.append(model.predict(series[time:time + window_size][np.newaxis]))\n",
        "\n",
        "forecast = forecast[split_time-window_size:]\n",
        "results = np.array(forecast)[:, 0, 0]\n",
        "\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "\n",
        "plot_series(time_valid, x_valid)\n",
        "plot_series(time_valid, results)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JpdfgEOfwVIX+bIqLB0xvs+1HjaPVkQrFWZWuTIJ4zEIpxaqudJM4K6b6zQM9+CXYcRMU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\nnAkhhBBCNBEJzoQQQgghmsj/DzXklJoqRNihAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "344471b5-d60c-41cb-def6-8f694c2c1e73",
        "id": "hR2BO0Dai_ZT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "tf.keras.metrics.mean_absolute_error(x_valid, results).numpy()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5.6874604"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1cfvdK9TVSnP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}